An image generally involves an extremely large amount of data. Consequently, images are often coded to reduce the amount of data needed to represent the image, for example prior to storage or transmission. Such coding is commonly referred to as image to compression. Image compression is commonly used in medical imaging, image archiving, aerial imaging, video, and computer generated document systems.
Some image compression systems use hierarchical coding or data structures, e.g., pyramid data structures to represent the image. With such image compression systems, a user may select the image data, out of the hierarchical coded image data, which matches the definition of the user""s own monitor.
In addition to hierarchical coding, image compression systems may also employ prediction encoding methods. Prediction encoding methods may involve predicting a pixel value of a target or object pixel from peripheral pixel values, determining a prediction error, and performing entropy encoding of a prediction error.
In accordance with a first aspect of the invention, a method and apparatus for use in processing an image receives data representative of at least a portion of one or more reconstructed versions of the image including data representative of a causal context of an object pixel of the image. The method and apparatus uses a plurality of predictors to generate a plurality of predictions for the object pixel and to generate a plurality of predictions for at least one pixel of the causal context, determines at least one measure of correlation between the plurality of predictions for the at least one pixel of the causal context and the data representative of the causal context, and provides a prediction for the object pixel computed as a weighted sum in accordance the at least one measure of correlation and a weighting policy.
In accordance with a second aspect of the invention, a method and apparatus for use in processing an image receives data representative of a first prediction of an object pixel and receives data representative of a plurality of reconstructed versions of an image including data representative of two or more causal contexts of the image from two or more resolutions of the image. The method and apparatus determines a context from the two or more causal contexts, and determines a substantially mean error for the context, and provides a second prediction for the object pixel in accordance with the data representative of the prediction for the object pixel and the substantially mean error for the context.
In accordance with a third aspect of the invention, a method and apparatus for use in processing an image receives data representative of one or more prediction differences versions of the image including data representative of a causal context prediction difference of an object prediction difference of the image. The method and apparatus uses a plurality of predictors to generate a plurality of predictions for the object prediction difference and to generate a plurality of predictions for at least one prediction difference of the causal context, determines at least one measure of correlation between the plurality of predictions for the at least one prediction difference and the data representative of the causal context prediction difference, and provides a prediction for the object prediction difference computed as a weighted sum in accordance the at least one measure of correlation and a weighting policy.
In accordance with a fourth aspect of the invention, a method and apparatus for use in processing an image receives data representative of a prediction of an object prediction difference and receives data representative of a plurality of reconstructed versions of an image including data representative of two or more causal contexts of the image from two or more resolutions of the image. The method and apparatus determines a context for the two or more causal contexts, and determines a substantially mean error for the context. The method and apparatus provides a prediction for the object prediction difference in accordance with the data representative of the prediction for the object prediction difference and the substantially mean error for the context.